1. Engineering Better Learning
Wagner • Leitner • Bier • Long
November 1, 2017 Philadelphia, PA

2. Engineering Better Learning  Wednesday, November 1, 2:00 - 2:50 pm, Room 204C
This session explores learning engineering, an emerging discipline built upon data science, learning analytics and design based research, to better understand how students learn and instructional strategies that enable optimal learning.
Using Harvard University Extension School's June 2017 workshop as a foundation, this session explores strengths and weaknesses associated with learning engineering as a framework for enabling data rich methods for transforming student success and institutional effectiveness.
Session participants will explore the topic: Does Learning Engineering represent the “next generation” of learning analytics in education?

3. Panel Participants Ellen Wagner, VP Research, Hobsons
Henry Leitner, Senior Lecturer on Computer Science at School of Engineering and Applied Sciences, and Chief Innovation Officer at Division of Continuing Education, Harvard University;
Norman Bier, Director of the Open Learning Initiative (OLI) and the Executive Director of the Simon Initiative at Carnegie Mellon University;
Phillip Long, Associate Provost for Learning Sciences, University of Texas at Austin

4. Some More on Learning Engineering
Learning engineering is characterized by evidence-based approaches, measurable and measured outcomes, and iterative processes (Saxberg, 2015).
Contributing disciplines include cognitive science, computer science (human-computer interaction, machine learning, artificial intelligence), cognitive psychology, education (psychometrics, educational psychology, learning sciences), and statistics.
Learning engineering can take many forms, ranging from data analysis of existing data gathered through students’ interaction with LMS to targeted a/b testing of the effects of a particular intervention implemented across online courses.  
Thus, learning engineers are professionals who understand learning science and apply data analytics and experimental design to help more students learn more effectively.